Duplicate from GV05/Marvel-Heroes-Classification-using-clip

Co-authored-by: Noam Shimshoviz <[email protected]>

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README.md

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+---
+title: Marvel Heroes Classification Using Clip
+emoji: 🐠
+colorFrom: purple
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.4
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: GV05/Marvel-Heroes-Classification-using-clip
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from transformers import CLIPProcessor, CLIPModel
+import gradio as gr
+
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+classes = ["Iron Man", "Captain America", "Thor", "Spider-Man", "Black Widow", "Black Panther","Hulk", "Ant-Man",
+           'Peggy Carter', "Daredevil", "Star-Lord", "Wong", "Doctor Strange","Nick Fury", "Gamora", "Jessica Jones",
+           "Nebula", "Falcon", "Winter Soldier", "Rocket", "Hawkeye"]
+text = [f"a photo of {x}" for x in classes]
+def predict(img):
+  inputs = processor(text=text, images=img, return_tensors="pt", padding=True)
+  outputs = model(**inputs)
+  logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+  probs = logits_per_image.softmax(dim=1).squeeze() # we can take the softmax to get the label probabilities
+  return {classes[i] : float(probs[i]) for i in range(len(probs))}
+
+title = "Marvel Heroes Classification"
+description = "Using clip for zero-shot classification"
+examples = ["black_panter.jpg"]
+gr.Interface(fn=predict, inputs = gr.inputs.Image(shape = (512,512)), outputs= gr.outputs.Label(),
+             examples=examples, title=title, description=description).launch(inline=False)

requirements.txt

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+transformers
+gradio
+torch